A Human-Centered and Adaptive Robotic System Using Deep Learning and Adaptive Predictive Controllers
Sari Toyoguchi*, Enrique Coronado**, and Gentiane Venture***
*Department of Mechanical Systems Engineering, Tokyo University of Agriculture and Technology
2-24-16 Nakamachi, Koganei, Tokyo 184-8588, Japan
**Industrial Cyber-Physical Systems Research Center, National Institute of Advanced Industrial Science and Technology (AIST)
2-4-7 Aomi, Koto-ku, Tokyo 135-0064, Japan
***Graduate School of Engineering, The University of Tokyo
7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan
The rise of single-person households coupled with a drop in social interaction due to the coronavirus disease 2019 (COVID-19) pandemic is triggering a loneliness pandemic. This social issue is producing mental health conditions (e.g., depression and stress) not only in the elderly population but also in young adults. In this context, social robots emerge as human-centered robotics technology that can potentially reduce mental health distress produced by social isolation. However, current robotics systems still do not reach a sufficient communication level to produce an effective coexistence with humans. This paper contributes to the ongoing efforts to produce a more seamless human-robot interaction. For this, we present a novel cognitive architecture that uses (i) deep learning methods for mood recognition from visual and voice modalities, (ii) personality and mood models for adaptation of robot behaviors, and (iii) adaptive generalized predictive controllers (AGPC) to produce suitable robot reactions. Experimental results indicate that our proposed system influenced people’s moods, potentially reducing stress levels during human-robot interaction.
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